PCAN

Official PyTorch implementation for the paper:

Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition, AAAI 2025.

🛠️ Installation

conda create --name openmmlab python=3.8 -y
conda activate openmmlab
conda install pytorch torchvision -c pytorch  # This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they match your environment.
pip install -U openmim
mim install mmengine
mim install mmcv
mim install mmdet  # optional
mim install mmpose  # optional
git clone https://github.com/kunli-cs/PCAN.git
cd ./mmaction2
pip install -v -e .

Training

TBD

🖊️ Citation

If you found this code useful, please consider cite:

@article{li2024prototypical,
  title={Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition},
  author={Li, Kun and Guo, Dan and Chen, Guoliang and Fan, Chunxiao and Xu, Jingyuan and Wu, Zhiliang and Fan, Hehe and Wang, Meng},
  journal={arXiv preprint arXiv:2412.14719},
  year={2024}
}
@article{guo2024benchmarking,
  title={Benchmarking Micro-action Recognition: Dataset, Methods, and Applications},
  author={Guo, Dan and Li, Kun and Hu, Bin and Zhang, Yan and Wang, Meng},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2024},
  volume={34},
  number={7},
  pages={6238-6252},
}
@misc{2020mmaction2,
    title={OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark},
    author={MMAction2 Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmaction2}},
    year={2020}
}

🤝 Acknowledgement

This code began with mmaction2. We thank the developers for doing most of the heavy-lifting.